Research Article
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Day-of-the-Week and Month-of-the-Year Effects in the Cryptocurrency Market

Year 2024, Volume: 11 Issue: 1, 346 - 365, 31.03.2024
https://doi.org/10.30798/makuiibf.1387108

Abstract

This study examines the day-of-the-week (DoW) and month-of-the-year (MoY) effects in the cryptocurrency market, with a focus on Bitcoin (BTC) and Ethereum (ETH). Due to the absence of a specific closing time in the cryptocurrency market, the closing time of the daily data is taken as 23:59 UTC. Initially, an appropriate volatility model for the cryptocurrency market is established using the GARCH, EGARCH, and TGARCH models. The most appropriate model for BTC is ARMA(1,0)-EGARCH(1,1) and ARMA(1,0)-GARCH(1,1) for ETH. The results of the analysis indicate a leverage effect in the cryptocurrency market, where negative shocks cause a more significant increase in volatility than positive shocks. Based on this volatility structure, the DoW and MoY are analyzed. For BTC, returns on other days are lower compared to Mondays. However, for ETH, returns on Thursdays are lower than those on Mondays. In terms of volatility, both BTC and ETH show that the highest volatility occurs on Mondays. For the MoY effect, neither BTC nor ETH don’t exhibit a significant effect in the mean equation. Nevertheless, the variance equation indicates that January has higher volatility compared to other months, indicating the presence of a MoY effect in terms of volatility.

References

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  • Ahmed, W. M. (2020). Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin. Journal of Economics and Business, 108, 105886. https://doi.org/10.1016/j.jeconbus.2019.105886.
  • Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. https://doi.org/10.1016/j.econlet.2018.10.008.
  • Baur, D. G., Cahill, D., Godfrey, K., & Liu, Z. F. (2019). Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume. Finance Research Letters, 31, 78-92. https://doi.org/10.1016/j.frl.2019.04.023.
  • Berument, H., & Kiymaz, H. (2001). The day-of-the-week effect on stock market volatility. Journal of Economics and Finance, 25(2), 181-193. https://doi.org/10.1007/BF02744521.
  • Berument, M. H., & Dogan, N. (2012). Stock market return and volatility: day-of-the-week effect. Journal of Economics and Finance, 36, 282-302. https://doi.org/10.1007/s12197-009-9118-y.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1.
  • Bouoiyour, J., & Selmi, R. (2016). Bitcoin: A beginning of a new phase. Economics Bulletin, 36(3), 1430-1440.
  • Brooks, C. (2014). Introductory Econometrics for Finance. Cambridge University Press.
  • Caporale, G. M., & Plastun, A. (2019). The day-of-the-week effect in the cryptocurrency market. Finance Research Letters, 31. https://doi.org/10.1016/j.frl.2018.11.012.
  • CoinMarketCap. (2023). https://coinmarketcap.com/
  • Cross, F. (1973). The behavior of stock prices on Fridays and Mondays. Financial Analysts Journal, 29(6), 67-69. https://doi.org/10.2469/faj.v29.n6.67.
  • Décourt, R. F., Chohan, U. W., & Perugini, M. L. (2017). Bitcoin returns and the Monday effect. Horizontes Empresariales, 16(2).
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531.
  • Dorfleitner, G., & Lung, C. (2018). Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect. Journal of Asset Management, 19, 472-494. https://doi.org/10.1057/s41260-018-0093-8.
  • Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x.
  • Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075. https://doi.org/10.1016/j.ribaf.2019.101075.
  • Franke, J., Härdle, W. K., & Hafner, C. M. (2019). Financial Econometrics of Cryptocurrencies. Statistics of Financial Markets: An Introduction, 545-568.
  • French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics, 8(1), 55-69. https://doi.org/10.1016/0304-405X(80)90021-5.
  • Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics, 3(4), 739-753. https://doi.org/10.3934/QFE.2019.4.739.
  • Kaiser, L. (2019). Seasonality in cryptocurrencies. Finance Research Letters, 31, 232-238. https://doi.org/10.1016/j.frl.2018.11.007.
  • Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics, 12(1), 13-32. https://doi.org/10.1016/0304-405X(83)90025-9.
  • Kinateder, H., & Papavassiliou, V. G. (2021). Calendar effects in bitcoin returns and volatility. Finance Research Letters, 38, 101420. https://doi.org/10.1016/j.frl.2019.101420.
  • Kiymaz, H., & Berument, H. (2003). The day-of-the-week effect on stock market volatility and volume: International Evidence. Review of Financial Economics, 12(4), 363-380. https://doi.org/10.1016/S1058-3300(03)00038-7.
  • Köchling, G., Schmidtke, P., & Posch, P. N. (2020). Volatility forecasting accuracy for Bitcoin. Economics Letters, 191, 108836. https://doi.org/10.1016/j.econlet.2019.108836.
  • Le Tran, V., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35, 101382. https://doi.org/10.1016/j.frl.2019.101382.
  • Ma, D., & Tanizaki, H. (2019a). The day-of-the-week effect on Bitcoin return and volatility. Research in International Business and Finance, 49, 127-136. https://doi.org/10.1016/j.ribaf.2019.02.003.
  • Ma, D., & Tanizaki, H. (2019b). On the day-of-the-week effects of Bitcoin markets: international evidence. China Finance Review International, 9(4), 455-478. https://doi.org/10.1108/CFRI-12-2018-0158.
  • Mbanga, C. L. (2019). The day-of-the-week pattern of price clustering in Bitcoin. Applied Economics Letters, 26(10), 807-811. https://doi.org/10.1080/13504851.2018.1497844.
  • Mills, T. C., & Andrew Coutts, J. (1995). Calendar effects in the London Stock Exchange FTSE indices. The European Journal of Finance, 1(1), 79-93. https://doi.org/10.1080/13518479500000010.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370. https://doi.org/10.2307/2938260.
  • Ngunyi, A., Mundia, S., & Omari, C. (2019). Modelling volatility dynamics of cryptocurrencies using GARCH models. Journal of Mathematical Finance, 9, 591-615. https://doi.org/10.4236/jmf.2019.94030.
  • Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335.
  • Plastun, A., Drofa, A. O., & Klyushnik, T. V. (2019). Month-of-the-year effect in the cryptocurrency market and portfolio management. European Journal of Management Issues, 27(1-2), 29-35. https://doi.org/10.15421/191904.
  • Qi, T., Wang, T., Zhu, J., & Bai, R. (2020). The correlation and volatility between bitcoin and the blockchain index. International Journal of Crowd Science, 4(2), 103-115. https://doi.org/10.1108/IJCS-11-2019-0036.
  • Robiyanto, R., Susanto, Y. A., & Ernayani, R. (2019). Examining the day-of-the-week-effect and the-month-of-the-year-effect in cryptocurrency market. Jurnal Keuangan dan Perbankan, 23(3), 361-375. https://doi.org/10.26905/jkdp.v23i3.3005.
  • Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379-402. https://doi.org/10.1016/0304-405X(76)90028-3.
  • Tsay, R. S. (2010). Analysis of Financial Time Series. John Wiley & Sons.
  • Wajdi, M., Nadia, B., & Ines, G. (2020). Asymmetric effect and dynamic relationships over the cryptocurrencies market. Computers & Security, 96, 101860. https://doi.org/10.1016/j.cose.2020.101860.
  • Wang, J. N., Liu, H. C., Zhang, S., & Hsu, Y. T. (2021). How does the informed trading impact Bitcoin returns and volatility?. Applied Economics, 53(28), 3223-3233. https://doi.org/10.1080/00036846.2020.1814944.
  • Yaya, O. S., & Ogbonna, E. A. (2019). Do we experience day-of-the-week effects in returns and volatility of cryptocurrency?.
  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955. https://doi.org/10.1016/0165-1889(94)90039-6.
  • Zargar, F. N., & Kumar, D. (2019). Long range dependence in the Bitcoin market: A study based on high-frequency data. Physica A: Statistical Mechanics and its Applications, 515, 625-640. https://doi.org/10.1016/j.physa.2018.09.188.
  • Zhou, S. (2021). Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach. Empirical Economics, 60(2), 557-606. https://doi.org/10.1007/s00181-019-01776-4.
Year 2024, Volume: 11 Issue: 1, 346 - 365, 31.03.2024
https://doi.org/10.30798/makuiibf.1387108

Abstract

References

  • Aharon, D. Y., & Qadan, M. (2019). Bitcoin and the day-of-the-week effect. Finance Research Letters, 31. https://doi.org/10.1016/j.frl.2018.12.004.
  • Ahmed, W. M. (2020). Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin. Journal of Economics and Business, 108, 105886. https://doi.org/10.1016/j.jeconbus.2019.105886.
  • Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. https://doi.org/10.1016/j.econlet.2018.10.008.
  • Baur, D. G., Cahill, D., Godfrey, K., & Liu, Z. F. (2019). Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume. Finance Research Letters, 31, 78-92. https://doi.org/10.1016/j.frl.2019.04.023.
  • Berument, H., & Kiymaz, H. (2001). The day-of-the-week effect on stock market volatility. Journal of Economics and Finance, 25(2), 181-193. https://doi.org/10.1007/BF02744521.
  • Berument, M. H., & Dogan, N. (2012). Stock market return and volatility: day-of-the-week effect. Journal of Economics and Finance, 36, 282-302. https://doi.org/10.1007/s12197-009-9118-y.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1.
  • Bouoiyour, J., & Selmi, R. (2016). Bitcoin: A beginning of a new phase. Economics Bulletin, 36(3), 1430-1440.
  • Brooks, C. (2014). Introductory Econometrics for Finance. Cambridge University Press.
  • Caporale, G. M., & Plastun, A. (2019). The day-of-the-week effect in the cryptocurrency market. Finance Research Letters, 31. https://doi.org/10.1016/j.frl.2018.11.012.
  • CoinMarketCap. (2023). https://coinmarketcap.com/
  • Cross, F. (1973). The behavior of stock prices on Fridays and Mondays. Financial Analysts Journal, 29(6), 67-69. https://doi.org/10.2469/faj.v29.n6.67.
  • Décourt, R. F., Chohan, U. W., & Perugini, M. L. (2017). Bitcoin returns and the Monday effect. Horizontes Empresariales, 16(2).
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531.
  • Dorfleitner, G., & Lung, C. (2018). Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect. Journal of Asset Management, 19, 472-494. https://doi.org/10.1057/s41260-018-0093-8.
  • Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x.
  • Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075. https://doi.org/10.1016/j.ribaf.2019.101075.
  • Franke, J., Härdle, W. K., & Hafner, C. M. (2019). Financial Econometrics of Cryptocurrencies. Statistics of Financial Markets: An Introduction, 545-568.
  • French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics, 8(1), 55-69. https://doi.org/10.1016/0304-405X(80)90021-5.
  • Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics, 3(4), 739-753. https://doi.org/10.3934/QFE.2019.4.739.
  • Kaiser, L. (2019). Seasonality in cryptocurrencies. Finance Research Letters, 31, 232-238. https://doi.org/10.1016/j.frl.2018.11.007.
  • Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics, 12(1), 13-32. https://doi.org/10.1016/0304-405X(83)90025-9.
  • Kinateder, H., & Papavassiliou, V. G. (2021). Calendar effects in bitcoin returns and volatility. Finance Research Letters, 38, 101420. https://doi.org/10.1016/j.frl.2019.101420.
  • Kiymaz, H., & Berument, H. (2003). The day-of-the-week effect on stock market volatility and volume: International Evidence. Review of Financial Economics, 12(4), 363-380. https://doi.org/10.1016/S1058-3300(03)00038-7.
  • Köchling, G., Schmidtke, P., & Posch, P. N. (2020). Volatility forecasting accuracy for Bitcoin. Economics Letters, 191, 108836. https://doi.org/10.1016/j.econlet.2019.108836.
  • Le Tran, V., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35, 101382. https://doi.org/10.1016/j.frl.2019.101382.
  • Ma, D., & Tanizaki, H. (2019a). The day-of-the-week effect on Bitcoin return and volatility. Research in International Business and Finance, 49, 127-136. https://doi.org/10.1016/j.ribaf.2019.02.003.
  • Ma, D., & Tanizaki, H. (2019b). On the day-of-the-week effects of Bitcoin markets: international evidence. China Finance Review International, 9(4), 455-478. https://doi.org/10.1108/CFRI-12-2018-0158.
  • Mbanga, C. L. (2019). The day-of-the-week pattern of price clustering in Bitcoin. Applied Economics Letters, 26(10), 807-811. https://doi.org/10.1080/13504851.2018.1497844.
  • Mills, T. C., & Andrew Coutts, J. (1995). Calendar effects in the London Stock Exchange FTSE indices. The European Journal of Finance, 1(1), 79-93. https://doi.org/10.1080/13518479500000010.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370. https://doi.org/10.2307/2938260.
  • Ngunyi, A., Mundia, S., & Omari, C. (2019). Modelling volatility dynamics of cryptocurrencies using GARCH models. Journal of Mathematical Finance, 9, 591-615. https://doi.org/10.4236/jmf.2019.94030.
  • Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335.
  • Plastun, A., Drofa, A. O., & Klyushnik, T. V. (2019). Month-of-the-year effect in the cryptocurrency market and portfolio management. European Journal of Management Issues, 27(1-2), 29-35. https://doi.org/10.15421/191904.
  • Qi, T., Wang, T., Zhu, J., & Bai, R. (2020). The correlation and volatility between bitcoin and the blockchain index. International Journal of Crowd Science, 4(2), 103-115. https://doi.org/10.1108/IJCS-11-2019-0036.
  • Robiyanto, R., Susanto, Y. A., & Ernayani, R. (2019). Examining the day-of-the-week-effect and the-month-of-the-year-effect in cryptocurrency market. Jurnal Keuangan dan Perbankan, 23(3), 361-375. https://doi.org/10.26905/jkdp.v23i3.3005.
  • Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379-402. https://doi.org/10.1016/0304-405X(76)90028-3.
  • Tsay, R. S. (2010). Analysis of Financial Time Series. John Wiley & Sons.
  • Wajdi, M., Nadia, B., & Ines, G. (2020). Asymmetric effect and dynamic relationships over the cryptocurrencies market. Computers & Security, 96, 101860. https://doi.org/10.1016/j.cose.2020.101860.
  • Wang, J. N., Liu, H. C., Zhang, S., & Hsu, Y. T. (2021). How does the informed trading impact Bitcoin returns and volatility?. Applied Economics, 53(28), 3223-3233. https://doi.org/10.1080/00036846.2020.1814944.
  • Yaya, O. S., & Ogbonna, E. A. (2019). Do we experience day-of-the-week effects in returns and volatility of cryptocurrency?.
  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955. https://doi.org/10.1016/0165-1889(94)90039-6.
  • Zargar, F. N., & Kumar, D. (2019). Long range dependence in the Bitcoin market: A study based on high-frequency data. Physica A: Statistical Mechanics and its Applications, 515, 625-640. https://doi.org/10.1016/j.physa.2018.09.188.
  • Zhou, S. (2021). Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach. Empirical Economics, 60(2), 557-606. https://doi.org/10.1007/s00181-019-01776-4.
There are 44 citations in total.

Details

Primary Language English
Subjects Behavioural Finance, Finance, Financial Econometrics
Journal Section Research Articles
Authors

İbrahim Korkmaz Kahraman 0000-0001-5083-3586

Dündar Kök 0000-0002-5250-3369

Early Pub Date March 29, 2024
Publication Date March 31, 2024
Submission Date November 6, 2023
Acceptance Date March 9, 2024
Published in Issue Year 2024 Volume: 11 Issue: 1

Cite

APA Kahraman, İ. K., & Kök, D. (2024). Day-of-the-Week and Month-of-the-Year Effects in the Cryptocurrency Market. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(1), 346-365. https://doi.org/10.30798/makuiibf.1387108